Computational Modelling in Behavioural Neuroscience: Closing the Gap Between Neurophysiology and Behaviour

ISBN-13: 9781841697383 / Angielski / Twarda / 2009 / 376 str.

Dietmar Heinke;Eirini Mavritsaki

Opis

Classically, behavioural neuroscience theorizes about experimental evidence in a qualitative way. However, more recently there has been an increasing development of computational models of experimental results, and these models are often more clearly defined and more detailed than their qualitative counterparts. This book brings together contributions from leading researchers in this developing field.

Approaches in computational modelling range from those that look at detailed neurophysiological processes (but are indifferent to whole-system behaviour), to those that model a broad range of behavioural data (but are oblivious to neurophysiological details). In order to close the gap between neurophysiological process and human behaviour it may be necessary to connect both ends of that spectrum, such as modelling a broad range of behavioural data together with neurophysiological details. In order to work towards this objective and develop an integrative approach, the book contains chapters from computational modelling researchers working on different points of this spectrum.

Computational Modelling in Behavioural Neuroscience represents the state-of-the-art in the field through a unique collection of papers from the world's leading researchers in the area of computational modelling in behavioural neuroscience. The book targets a broad audience, from postgraduate students beginning to work in the field to experienced experimenters interested in an overview.

Spis treści

Mavritsaki, Heinke, Preface. Graham, Cutsuridis, Dynamical Information Processing in the CA1 Microcircuit of the Hippocampus. Thorpe, Why Connectionist Models Need Spikes. Deco, Rolls, Stochastic Neuro-Dynamical Computation of Brain Functions. Humphreys, Mavritsaki, Allen, Heinke, Deco, Application of Neural Level Model to Human Visual Search: Modelling the Whole System Behaviour, Neuropsychological Break Down and Neural Signal Response. Heinke, Mavritsaki, Backhaus, Kreyling, The Selective Attention for Identification Model (SAIM): A Framework for Closing the Gap Between Behaviour and Neurological Level. Gurney, Computational Models in Neuroscience: From Membrane to Robots. Zhaoping, May, Koene, Some Finger Prints of V1 Mechanisms in the Bottom up Saliency for Visual Selection. Trappenberg, Decision Making and Population Decoding with Strongly Inhibitory Neural Field Models. Bullinaria, The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity. Ward, Ward, Selective Attention in Linked, Minimally Cognitive Agents. Kropff, Full Solution for the Storage of Correlated Memories in an Autoassociative Memory. Mozer, Wilder, A Unified Theory of Exogenous and Endogenous Attentional Control. Friston, Stephan, Kiebel, Free-Energy, Value and Neuronal Systems. Sloman, Architecture and Representation Requirements for Seeing Processes and Affordances. Heinke, Computational Modelling in Behavioural Neuroscience: Methodologies and Approaches - Minutes of Discussions at the Workshop in Birmingham, UK in May 2007